We develop a Bayesian nonparametric model to assess the effect of systematic risks on multiple financial markets, and apply it to understand the behavior of the S&P500 sector indexes between January 1, 2000 and December 31, 2011. More than prediction, our main goal is to understand the evolution...

Models commonly employed to fit current claims data and predict future claims are often parametric and relatively inflexible. An incorrect model assumption can cause model misspecification which leads to reduced profits at best and dangerous, unanticipated risk exposure at worst. Even mixture...

We develop a Bayesian nonparametric mixture modeling framework for replicated count responses in dose-response settings. We explore this methodology for modeling and risk assessment in developmental toxicity studies, where the primary objective is to determine the relationship between the level...